How social learning enhances-or undermines-efficiency and flexibility in collective decision-making under uncertainty.

Balancing efficiency and flexibility in collective decision-making is increasingly critical in modern societies characterized by rapid sociocultural and technological change. Recent research in cognitive neuroscience has proposed two contrasting computational algorithms for social learning: value shaping (VS) and decision biasing (DB). VS posits that others' choices serve as "pseudo-rewards" that directly shape an observer's valuations, leading them to prefer popular options even in the absence of outcome feedback. In contrast, DB confines the influence of social information to behavior-observers may imitate popular actions, but they update their valuations solely through personal experience. Although both algorithms facilitate individual adaptation under uncertainty, their interactive dynamics and group-level consequences remain largely unexplored. To address this gap, we developed computational models of VS and DB within a reinforcement learning framework and conducted agent-based simulations to examine collective performance in uncertain and dynamically changing environments. The results reveal a trade-off: VS enables rapid convergence and high efficiency in stable contexts, whereas DB promotes greater adaptability under environmental volatility. These differences are amplified in larger groups, particularly under strong majority influence. Importantly, evolutionary analyses indicate that both learning types can coexist stably, allowing their complementary strengths to enhance group performance. Together, our findings provide a computational and evolutionary account of how social learning can both enhance and impair collective intelligence-and suggest design principles for fostering resilient collective decision systems in human and AI societies facing rapid change.